{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a321ac97-bf54-4bb2-9a69-71e89cdae8da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "erp_order 模拟数据示例：\n",
      "   id internal_order_number online_order_number store_name  \\\n",
      "0   1       IN2022050500001     ON2022050500001      天猫旗舰店   \n",
      "1   2       IN2022051200002     ON2022051200002      京东自营店   \n",
      "2   3       IN2022011900003     ON2022011900003    抖音官方旗舰店   \n",
      "3   4       IN2022032900004     ON2022032900004      京东自营店   \n",
      "4   5       IN2022030800005     ON2022030800005      京东自营店   \n",
      "\n",
      "       full_channel_user_id       shipping_date        payment_date  \\\n",
      "0  17b1afb06407b7b77b2e46f0 2022-05-07 13:24:41 2022-05-05 13:24:41   \n",
      "1  efb0f733d8742409c92cd0a3 2022-05-17 00:02:53 2022-05-13 00:02:53   \n",
      "2  6636fb60d4563bcd72df1741 2022-01-26 13:45:05 2022-01-19 13:45:05   \n",
      "3  6e63aca43636b2c521cb060c 2022-03-30 08:15:57 2022-03-29 08:15:57   \n",
      "4  48526f5d429253ceee7d8faa 2022-03-11 17:31:56 2022-03-08 17:31:56   \n",
      "\n",
      "   payable_amount  paid_amount status  ... original_online_order_number  \\\n",
      "0          350.28       318.30    已付款  ...              ON2022050500001   \n",
      "1          388.05       365.64    已发货  ...              ON2022051200002   \n",
      "2          482.10       437.69    已发货  ...              ON2022011900003   \n",
      "3          667.48       625.08    已发货  ...              ON2022032900004   \n",
      "4         1280.13      1181.44    待付款  ...              ON2022030800005   \n",
      "\n",
      "         sku quantity unit_price product_name color_and_spec product_amount  \\\n",
      "0  SKU629903        1     350.28        自己精华液        #03 大容量         350.28   \n",
      "1  SKU197251        1     388.05        企业精华液        #02 加厚款         388.05   \n",
      "2  SKU838797        5      96.42        专业精华液        #01 标准款         482.10   \n",
      "3  SKU738720        4     166.87        简介精华液        #03 大容量         667.48   \n",
      "4  SKU694731        3     426.71        自己精华液        #03 大容量        1280.13   \n",
      "\n",
      "  original_price  is_gift  refund_status  \n",
      "0         506.54        是          未申请退款  \n",
      "1         419.03        是          未申请退款  \n",
      "2         704.42        是          未申请退款  \n",
      "3         879.63        否          未申请退款  \n",
      "4        1776.82        否          未申请退款  \n",
      "\n",
      "[5 rows x 26 columns] \n",
      "\n",
      "共有行数： 1200\n",
      "\n",
      "项目进度数据：\n",
      "   项目名称       开始日期  项目天数  完成度(数值)  完成度   进行天数       结束日期\n",
      "0  制定计划 2022-03-01    11     0.51  51%   5.61 2022-03-12\n",
      "1  方案设计 2022-03-13     8     0.32  32%   2.56 2022-03-21\n",
      "2  资源调配 2022-03-22    10     0.21  21%   2.10 2022-04-01\n",
      "3  第一阶段 2022-04-02    13     0.85  85%  11.05 2022-04-15\n",
      "4  第二阶段 2022-04-16    24     0.36  36%   8.64 2022-05-10\n",
      "5  第三阶段 2022-05-11    14     0.68  68%   9.52 2022-05-25\n",
      "6  项目总结 2022-05-26     7     0.68  68%   4.76 2022-06-02\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "\"\"\"\n",
    "2022年化妆品类目采购项目进度 甘特图（模拟 ERP 数据 + 可视化）\n",
    "\n",
    "使用说明：\n",
    "1. 需要安装：pandas、numpy、matplotlib、faker\n",
    "   pip install pandas numpy matplotlib Faker\n",
    "2. 直接运行即可，前半部分用 Faker 生成模拟 ERP 数据（>1000 条），\n",
    "   后半部分生成与 Excel 模板一致的数据表并画出甘特图。\n",
    "\"\"\"\n",
    "\n",
    "import random\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from faker import Faker\n",
    "\n",
    "# ----------------------- 0. Matplotlib 全局字体设置 -----------------------\n",
    "# 解决中文显示和负号问题（Windows 建议用黑体或雅黑）\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'Arial Unicode MS']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# ----------------------- 1. 使用 Faker 生成模拟数据 -----------------------\n",
    "\n",
    "fake = Faker(\"zh_CN\")\n",
    "Faker.seed(42)\n",
    "random.seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "\n",
    "def gen_erp_order_data(n_rows: int = 1200) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    按照 erp_order 表结构，生成结构合理、字段类型匹配的模拟数据（不少于 1000 条）\n",
    "    这里只生成核心字段，其他字段可根据需要自行扩展\n",
    "    \"\"\"\n",
    "    stores = [\"天猫旗舰店\", \"京东自营店\", \"抖音官方旗舰店\", \"小红书旗舰店\", \"微信小程序店\"]\n",
    "    platforms = [\"天猫\", \"京东\", \"抖音\", \"小红书\", \"微信小程序\"]\n",
    "    provinces = [\"北京\", \"上海\", \"广东\", \"浙江\", \"江苏\", \"四川\", \"重庆\", \"湖北\", \"湖南\", \"山东\"]\n",
    "    cities = {\n",
    "        \"北京\": [\"北京\"],\n",
    "        \"上海\": [\"上海\"],\n",
    "        \"广东\": [\"广州\", \"深圳\", \"佛山\"],\n",
    "        \"浙江\": [\"杭州\", \"宁波\", \"温州\"],\n",
    "        \"江苏\": [\"南京\", \"苏州\", \"无锡\"],\n",
    "        \"四川\": [\"成都\", \"绵阳\"],\n",
    "        \"重庆\": [\"重庆\"],\n",
    "        \"湖北\": [\"武汉\", \"襄阳\"],\n",
    "        \"湖南\": [\"长沙\", \"株洲\"],\n",
    "        \"山东\": [\"济南\", \"青岛\"],\n",
    "    }\n",
    "    statuses = [\"待付款\", \"已付款\", \"已发货\", \"已完成\", \"已取消\"]\n",
    "    refund_statuses = [\"未申请退款\", \"申请中\", \"成功退款\", \"退款关闭\"]\n",
    "    is_gift_choices = [\"是\", \"否\"]\n",
    "\n",
    "    start_dt = datetime(2022, 1, 1)\n",
    "    end_dt = datetime(2022, 6, 30)\n",
    "\n",
    "    rows = []\n",
    "    for i in range(1, n_rows + 1):\n",
    "        store_name = random.choice(stores)\n",
    "        platform = random.choice(platforms)\n",
    "\n",
    "        # 下单时间\n",
    "        order_time = fake.date_time_between(start_date=start_dt, end_date=end_dt)\n",
    "\n",
    "        # 付款时间（下单后 0~1 天内）\n",
    "        payment_date = order_time + timedelta(\n",
    "            minutes=random.randint(5, 60 * 24)\n",
    "        )\n",
    "\n",
    "        # 发货时间（付款后 0~7 天内，部分订单可能未发货）\n",
    "        if random.random() < 0.9:\n",
    "            shipping_date = payment_date + timedelta(days=random.randint(0, 7))\n",
    "        else:\n",
    "            shipping_date = None\n",
    "\n",
    "        quantity = random.randint(1, 5)\n",
    "        unit_price = round(random.uniform(39, 499), 2)\n",
    "        product_amount = round(unit_price * quantity, 2)\n",
    "\n",
    "        # 原价略高于实际成交价\n",
    "        original_price = round(product_amount * random.uniform(1.0, 1.5), 2)\n",
    "\n",
    "        payable_amount = product_amount\n",
    "        # 有少量优惠\n",
    "        paid_amount = round(payable_amount * random.uniform(0.9, 1.0), 2)\n",
    "\n",
    "        province = random.choice(provinces)\n",
    "        city = random.choice(cities[province])\n",
    "\n",
    "        refund_status = random.choices(\n",
    "            refund_statuses, weights=[0.75, 0.05, 0.15, 0.05], k=1\n",
    "        )[0]\n",
    "\n",
    "        row = {\n",
    "            \"id\": i,\n",
    "            \"internal_order_number\": f\"IN{order_time.strftime('%Y%m%d')}{i:05d}\",\n",
    "            \"online_order_number\": f\"ON{order_time.strftime('%Y%m%d')}{i:05d}\",\n",
    "            \"store_name\": store_name,\n",
    "            \"full_channel_user_id\": fake.sha1()[:24],\n",
    "            \"shipping_date\": shipping_date,\n",
    "            \"payment_date\": payment_date,\n",
    "            \"payable_amount\": payable_amount,\n",
    "            \"paid_amount\": paid_amount,\n",
    "            \"status\": random.choice(statuses),\n",
    "            \"consignee\": fake.name(),\n",
    "            \"spu\": f\"SPU{random.randint(1000, 9999)}\",\n",
    "            \"order_time\": order_time,\n",
    "            \"province\": province,\n",
    "            \"city\": city,\n",
    "            \"platform\": platform,\n",
    "            \"original_online_order_number\": f\"ON{order_time.strftime('%Y%m%d')}{i:05d}\",\n",
    "            \"sku\": f\"SKU{random.randint(100000, 999999)}\",\n",
    "            \"quantity\": quantity,\n",
    "            \"unit_price\": unit_price,\n",
    "            \"product_name\": fake.word() + \"精华液\",\n",
    "            \"color_and_spec\": random.choice([\"#01 标准款\", \"#02 加厚款\", \"#03 大容量\"]),\n",
    "            \"product_amount\": product_amount,\n",
    "            \"original_price\": original_price,\n",
    "            \"is_gift\": random.choices(is_gift_choices, weights=[0.1, 0.9], k=1)[0],\n",
    "            \"refund_status\": refund_status,\n",
    "        }\n",
    "        rows.append(row)\n",
    "\n",
    "    df = pd.DataFrame(rows)\n",
    "    return df\n",
    "\n",
    "\n",
    "# 生成模拟 erp_order 数据（这里生成 1200 条）\n",
    "erp_order_df = gen_erp_order_data(1200)\n",
    "print(\"erp_order 模拟数据示例：\")\n",
    "print(erp_order_df.head(), \"\\n\")\n",
    "print(\"共有行数：\", len(erp_order_df))\n",
    "\n",
    "# ----------------------- 2. 构造项目进度数据（与 Excel 一致） -----------------------\n",
    "\n",
    "# 项目表数据（完全按照截图中表格设置）\n",
    "project_data = {\n",
    "    \"项目名称\": [\"制定计划\", \"方案设计\", \"资源调配\", \"第一阶段\", \"第二阶段\", \"第三阶段\", \"项目总结\"],\n",
    "    \"开始日期\": [\n",
    "        \"2022/3/1\",\n",
    "        \"2022/3/13\",\n",
    "        \"2022/3/22\",\n",
    "        \"2022/4/2\",\n",
    "        \"2022/4/16\",\n",
    "        \"2022/5/11\",\n",
    "        \"2022/5/26\",\n",
    "    ],\n",
    "    \"项目天数\": [11, 8, 10, 13, 24, 14, 7],\n",
    "    \"完成度(数值)\": [0.51, 0.32, 0.21, 0.85, 0.36, 0.68, 0.68],\n",
    "    \"完成度\": [\"51%\", \"32%\", \"21%\", \"85%\", \"36%\", \"68%\", \"68%\"],\n",
    "    # 下面两列直接按照截图中的值设置，保证完全一致\n",
    "    \"进行天数\": [5.61, 2.56, 2.10, 11.05, 8.64, 9.52, 4.76],\n",
    "    \"结束日期\": [\n",
    "        \"2022/3/12\",\n",
    "        \"2022/3/21\",\n",
    "        \"2022/4/1\",\n",
    "        \"2022/4/15\",\n",
    "        \"2022/5/10\",\n",
    "        \"2022/5/25\",\n",
    "        \"2022/6/2\",\n",
    "    ],\n",
    "}\n",
    "\n",
    "project_df = pd.DataFrame(project_data)\n",
    "project_df[\"开始日期\"] = pd.to_datetime(project_df[\"开始日期\"])\n",
    "project_df[\"结束日期\"] = pd.to_datetime(project_df[\"结束日期\"])\n",
    "\n",
    "print(\"\\n项目进度数据：\")\n",
    "print(project_df)\n",
    "\n",
    "# ----------------------- 3. 绘制甘特图（还原 Excel 风格） -----------------------\n",
    "\n",
    "# 颜色配置（深蓝背景 + 高亮蓝色进度条）\n",
    "bg_color = \"#101738\"        # 画布深蓝背景\n",
    "bar_color = \"#00a8ff\"      # 任务条亮蓝色\n",
    "vline_color = \"#8089b3\"    # 竖向虚线\n",
    "text_color = \"#ffffff\"     # 白色文字\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12, 5), dpi=120)\n",
    "fig.patch.set_facecolor(bg_color)\n",
    "ax.set_facecolor(bg_color)\n",
    "\n",
    "# Y 轴位置（从上到下依次排布）\n",
    "y_pos = np.arange(len(project_df))[::-1]  # 反转，使“制定计划”在最上方\n",
    "ax.set_yticks(y_pos)\n",
    "ax.set_yticklabels(project_df[\"项目名称\"], color=text_color, fontsize=11)\n",
    "\n",
    "# 绘制每一条任务进度条\n",
    "for i, row in project_df.iterrows():\n",
    "    start = row[\"开始日期\"]\n",
    "    duration = row[\"项目天数\"]  # 直接用项目天数控制长度\n",
    "    y = y_pos[i]\n",
    "\n",
    "    ax.barh(\n",
    "        y=y,\n",
    "        width=duration,\n",
    "        left=start,\n",
    "        height=0.6,\n",
    "        color=bar_color,\n",
    "        align=\"center\",\n",
    "        edgecolor=\"none\",\n",
    "    )\n",
    "\n",
    "    # 在条形中间写上完成度百分比\n",
    "    center = start + timedelta(days=duration / 2)\n",
    "    ax.text(\n",
    "        center,\n",
    "        y,\n",
    "        row[\"完成度\"],\n",
    "        color=text_color,\n",
    "        fontsize=10,\n",
    "        ha=\"center\",\n",
    "        va=\"center\",\n",
    "    )\n",
    "\n",
    "# 设置 X 轴为顶部，刻度与 Excel 一致\n",
    "tick_labels = [\n",
    "    \"2022/3/1\",\n",
    "    \"2022/3/16\",\n",
    "    \"2022/3/31\",\n",
    "    \"2022/4/15\",\n",
    "    \"2022/4/30\",\n",
    "    \"2022/5/15\",\n",
    "    \"2022/5/30\",\n",
    "    \"2022/6/14\",\n",
    "]\n",
    "tick_dates = pd.to_datetime(tick_labels)\n",
    "\n",
    "ax.set_xticks(tick_dates)\n",
    "ax.set_xticklabels(tick_labels, color=text_color, fontsize=10)\n",
    "\n",
    "# 在每个刻度画一条竖向虚线\n",
    "for d in tick_dates:\n",
    "    ax.axvline(\n",
    "        d,\n",
    "        linestyle=\"--\",\n",
    "        linewidth=0.7,\n",
    "        color=vline_color,\n",
    "        alpha=0.8,\n",
    "    )\n",
    "\n",
    "# X 轴放在顶部，底部刻度去掉\n",
    "ax.xaxis.tick_top()\n",
    "ax.xaxis.set_label_position(\"top\")\n",
    "\n",
    "# X 轴范围略微留出左右空白\n",
    "xmin = pd.to_datetime(\"2022-03-01\")\n",
    "xmax = pd.to_datetime(\"2022-06-14\")\n",
    "ax.set_xlim(xmin - timedelta(days=2), xmax + timedelta(days=2))\n",
    "\n",
    "# 去掉边框\n",
    "for spine in ax.spines.values():\n",
    "    spine.set_visible(False)\n",
    "\n",
    "# Y 轴刻度线去掉\n",
    "ax.tick_params(axis=\"y\", length=0)\n",
    "\n",
    "# 标题设置（居中、大号白色粗体）\n",
    "fig.suptitle(\n",
    "    \"2022年化妆品类目采购项目进度\",\n",
    "    fontsize=20,\n",
    "    color=text_color,\n",
    "    fontweight=\"bold\",\n",
    "    y=0.96,\n",
    ")\n",
    "\n",
    "plt.tight_layout(rect=[0, 0, 1, 0.9])\n",
    "\n",
    "# 保存图片，也可以直接 plt.show()\n",
    "plt.savefig(\"project_progress_gantt.png\", dpi=200, facecolor=bg_color)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "566df7bf-5450-4e07-87f5-7f48c1f95290",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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